Accelerating CUDA graph algorithms at maximum warp
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Best-first heuristic search for multicore machines
Journal of Artificial Intelligence Research
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This paper presents a parallel external-memory algorithm for performing a breadth-first traversal of an implicit graph on a cluster of workstations. The algorithm is a parallel version of the sorting-based external-memory frontier breadthfirst traversal with delayed duplicate detection algorithm. The algorithm distributes the workload according to intervals that are computed at runtime via a sampling-based process.We present an experimental evaluation of the algorithm where we compare its performance to that of its sequential counterpart on the implicit graphs of two classic planning problems. The speedups attained by the algorithm over its sequential counterpart are consistently near linear and frequently above linear. Analysis reveals that the algorithm is proficient at distributing the workload and that increasing the number of samples obtained by the sampling-based process improves workload distribution. Analysis also reveals that the algorithm benefits from the caching of external memory in internal memory that is done by the operating system.